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The Positive Equality approach identifies terms that have a certain characteristic in the original formula (before the reduction to pure Equality Logic) and replaces them with unique constants.
This method identifies terms that are over-represented within a given set of proteins compared with the whole proteome.
Next, it identifies terms in the other ontologies that are annotated to these genes and the process is repeated to expand the gene list.
This condenses redundant categories, identifies terms containing a smaller number of genes that on their own would require higher fold enrichments to reach statistical significance, and makes interpreting the results much easier.
This ensures that we can identify when our system identifies terms that are closely related to the terms used in a reference annotation even when they are not linked through a subsumption relationship.
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Since our goal is to identify terms and research topics, unigrams could be very ambiguous.
All identified terms are presented in Figure 1. Figure 1 Related concepts.
As a next step, the databases were screened regarding all identified terms from the last step.
The statistical approach used the NSP package to identify terms that contained an absolute frequency superior to a given value.
Thus, we address the limitation of existing TE systems, which are primarily designed to identify terms with 2 words.
Accordingly, the literature focus was extended to all identified papers in BSC and Google Scholar to get a comprehensive and detailed overview of all identified terms.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com